Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "243" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 30 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460013 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.066743 | -1.036873 | 0.762213 | -1.364736 | 1.876742 | -0.417771 | -1.336687 | -0.070429 | 0.4314 | 0.5534 | 0.3574 | nan | nan |
| 2460012 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.000055 | -1.090988 | 0.589798 | -1.505508 | 1.753706 | -0.295663 | -1.272303 | -0.251606 | 0.4321 | 0.5562 | 0.3520 | nan | nan |
| 2460011 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 15.577355 | -1.109976 | 1.453064 | -1.948931 | 7.994876 | -0.600949 | 20.339347 | -0.047541 | 0.4766 | 0.5559 | 0.3660 | nan | nan |
| 2460010 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.387735 | -0.794347 | 1.586901 | -1.452470 | 5.864437 | -1.188514 | 21.242586 | -0.114385 | 0.4644 | 0.5698 | 0.3727 | nan | nan |
| 2460009 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.630023 | -1.461860 | 1.170478 | -1.548941 | 3.079333 | -0.487270 | -0.299619 | -0.139746 | 0.4533 | 0.5757 | 0.3776 | nan | nan |
| 2460008 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 26.050259 | -1.132771 | 1.230757 | -1.716936 | 2.540372 | -0.741322 | 0.567949 | -1.358280 | 0.4905 | 0.6160 | 0.3386 | nan | nan |
| 2460007 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.576456 | -0.919178 | 0.752056 | -1.168526 | 1.983611 | -0.613492 | -1.001724 | 0.117241 | 0.4506 | 0.5780 | 0.3609 | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 0.08% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.4802 | 0.6034 | 0.3291 | nan | nan |
| 2459998 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 18.965366 | -1.006053 | 0.851967 | -1.180011 | 2.243730 | -0.222856 | -0.550485 | 0.341130 | 0.4556 | 0.5862 | 0.3793 | nan | nan |
| 2459997 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.317753 | -1.120882 | 1.108944 | -1.180915 | 2.816525 | -0.813413 | -1.583066 | -0.066349 | 0.4647 | 0.6004 | 0.3828 | nan | nan |
| 2459996 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.821633 | -1.374777 | 0.986236 | -1.297616 | 2.739661 | -1.044660 | 0.346544 | -0.319375 | 0.4789 | 0.6093 | 0.3930 | nan | nan |
| 2459995 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.580056 | -1.166194 | 1.015272 | -1.608310 | 2.471389 | -0.094844 | -0.924579 | -0.364755 | 0.4661 | 0.6023 | 0.3819 | nan | nan |
| 2459994 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.785811 | -0.990931 | 1.042544 | -1.237249 | 2.784365 | -0.934426 | -1.075764 | -0.223917 | 0.4601 | 0.5941 | 0.3779 | nan | nan |
| 2459993 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 25.702954 | -1.059182 | 1.353535 | -1.394347 | 3.706782 | -0.335645 | -0.392696 | -0.049349 | 0.4404 | 0.5933 | 0.3894 | nan | nan |
| 2459991 | RF_ok | 100.00% | 99.89% | 99.95% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.4806 | 0.2666 | 0.3979 | nan | nan |
| 2459990 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 22.480895 | -0.455912 | 1.242362 | -1.368267 | 3.474895 | -1.010285 | -1.456681 | -0.255564 | 0.4543 | 0.5858 | 0.3803 | nan | nan |
| 2459989 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 22.410707 | -0.501629 | 1.341922 | -1.017412 | 2.897340 | -0.804981 | -0.975323 | -0.166219 | 0.4553 | 0.5891 | 0.3820 | nan | nan |
| 2459988 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 26.265284 | -0.721889 | 1.250348 | -1.484513 | 4.040368 | -1.172142 | -1.019285 | -0.110985 | 0.4570 | 0.5923 | 0.3746 | nan | nan |
| 2459987 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.045447 | -0.973141 | 1.084995 | -1.322734 | 2.197197 | -0.563488 | -1.045006 | 0.351328 | 0.4627 | 0.5955 | 0.3741 | nan | nan |
| 2459986 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 25.629063 | -0.955506 | 1.203189 | -1.537571 | 3.656961 | -1.334684 | 1.246472 | -1.293508 | 0.4910 | 0.6261 | 0.3359 | nan | nan |
| 2459985 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.719510 | -1.001541 | 1.021288 | -1.405942 | 2.406059 | -1.174430 | -0.486365 | -0.025118 | 0.4677 | 0.5983 | 0.3797 | nan | nan |
| 2459984 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 36.515554 | -0.839168 | 0.640040 | -1.432858 | 12.636936 | -1.705085 | 5.398249 | -0.768421 | 0.4148 | 0.6175 | 0.3793 | nan | nan |
| 2459983 | RF_ok | 100.00% | 6.59% | 0.00% | 0.00% | - | - | 44.289744 | -0.696904 | 0.374740 | -1.428225 | 17.179438 | -1.226231 | 23.155922 | -0.934530 | 0.3720 | 0.6254 | 0.3864 | nan | nan |
| 2459982 | RF_ok | 100.00% | 0.11% | 0.00% | 0.00% | - | - | 57.790797 | -0.267569 | 0.288324 | -0.936288 | 5.683998 | -0.842556 | 4.822499 | -0.831822 | 0.4250 | 0.6754 | 0.3996 | nan | nan |
| 2459981 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 51.674956 | -0.435476 | 0.994757 | -1.494027 | 8.404720 | -1.405663 | 39.218672 | 0.554868 | 0.3749 | 0.5953 | 0.4207 | nan | nan |
| 2459980 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 50.476750 | -0.777554 | 0.700655 | -1.453843 | 5.387327 | -1.426071 | 1.654094 | -1.295189 | 0.4796 | 0.6446 | 0.3552 | nan | nan |
| 2459979 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 52.370597 | -0.507453 | 0.659828 | -1.446666 | 6.989762 | -1.133232 | 3.760459 | 0.088175 | 0.3990 | 0.5907 | 0.4128 | nan | nan |
| 2459978 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 53.730029 | -0.487011 | 0.824703 | -1.573082 | 5.161532 | -1.287010 | 4.751616 | 0.047776 | 0.3970 | 0.5902 | 0.4188 | nan | nan |
| 2459977 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 52.296103 | -0.627271 | 0.627492 | -1.463131 | 8.819396 | -1.557776 | 15.888445 | -0.271163 | 0.3618 | 0.5518 | 0.3831 | nan | nan |
| 2459976 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 54.348194 | -0.570096 | 0.759778 | -1.606147 | 4.705971 | -0.809330 | -0.778344 | 0.081211 | 0.4035 | 0.5986 | 0.4157 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 21.066743 | 21.066743 | -1.036873 | 0.762213 | -1.364736 | 1.876742 | -0.417771 | -1.336687 | -0.070429 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 20.000055 | 20.000055 | -1.090988 | 0.589798 | -1.505508 | 1.753706 | -0.295663 | -1.272303 | -0.251606 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Temporal Discontinuties | 20.339347 | 15.577355 | -1.109976 | 1.453064 | -1.948931 | 7.994876 | -0.600949 | 20.339347 | -0.047541 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Temporal Discontinuties | 21.242586 | 20.387735 | -0.794347 | 1.586901 | -1.452470 | 5.864437 | -1.188514 | 21.242586 | -0.114385 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 20.630023 | 20.630023 | -1.461860 | 1.170478 | -1.548941 | 3.079333 | -0.487270 | -0.299619 | -0.139746 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 26.050259 | -1.132771 | 26.050259 | -1.716936 | 1.230757 | -0.741322 | 2.540372 | -1.358280 | 0.567949 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 20.576456 | 20.576456 | -0.919178 | 0.752056 | -1.168526 | 1.983611 | -0.613492 | -1.001724 | 0.117241 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 18.965366 | 18.965366 | -1.006053 | 0.851967 | -1.180011 | 2.243730 | -0.222856 | -0.550485 | 0.341130 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 20.317753 | 20.317753 | -1.120882 | 1.108944 | -1.180915 | 2.816525 | -0.813413 | -1.583066 | -0.066349 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 20.821633 | 20.821633 | -1.374777 | 0.986236 | -1.297616 | 2.739661 | -1.044660 | 0.346544 | -0.319375 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 21.580056 | 21.580056 | -1.166194 | 1.015272 | -1.608310 | 2.471389 | -0.094844 | -0.924579 | -0.364755 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 21.785811 | 21.785811 | -0.990931 | 1.042544 | -1.237249 | 2.784365 | -0.934426 | -1.075764 | -0.223917 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 25.702954 | 25.702954 | -1.059182 | 1.353535 | -1.394347 | 3.706782 | -0.335645 | -0.392696 | -0.049349 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 22.480895 | -0.455912 | 22.480895 | -1.368267 | 1.242362 | -1.010285 | 3.474895 | -0.255564 | -1.456681 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 22.410707 | -0.501629 | 22.410707 | -1.017412 | 1.341922 | -0.804981 | 2.897340 | -0.166219 | -0.975323 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 26.265284 | -0.721889 | 26.265284 | -1.484513 | 1.250348 | -1.172142 | 4.040368 | -0.110985 | -1.019285 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 21.045447 | 21.045447 | -0.973141 | 1.084995 | -1.322734 | 2.197197 | -0.563488 | -1.045006 | 0.351328 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 25.629063 | -0.955506 | 25.629063 | -1.537571 | 1.203189 | -1.334684 | 3.656961 | -1.293508 | 1.246472 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 23.719510 | -1.001541 | 23.719510 | -1.405942 | 1.021288 | -1.174430 | 2.406059 | -0.025118 | -0.486365 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 36.515554 | 36.515554 | -0.839168 | 0.640040 | -1.432858 | 12.636936 | -1.705085 | 5.398249 | -0.768421 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 44.289744 | 44.289744 | -0.696904 | 0.374740 | -1.428225 | 17.179438 | -1.226231 | 23.155922 | -0.934530 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 57.790797 | 57.790797 | -0.267569 | 0.288324 | -0.936288 | 5.683998 | -0.842556 | 4.822499 | -0.831822 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 51.674956 | -0.435476 | 51.674956 | -1.494027 | 0.994757 | -1.405663 | 8.404720 | 0.554868 | 39.218672 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 50.476750 | -0.777554 | 50.476750 | -1.453843 | 0.700655 | -1.426071 | 5.387327 | -1.295189 | 1.654094 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 52.370597 | 52.370597 | -0.507453 | 0.659828 | -1.446666 | 6.989762 | -1.133232 | 3.760459 | 0.088175 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 53.730029 | -0.487011 | 53.730029 | -1.573082 | 0.824703 | -1.287010 | 5.161532 | 0.047776 | 4.751616 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 52.296103 | 52.296103 | -0.627271 | 0.627492 | -1.463131 | 8.819396 | -1.557776 | 15.888445 | -0.271163 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 243 | N19 | RF_ok | ee Shape | 54.348194 | -0.570096 | 54.348194 | -1.606147 | 0.759778 | -0.809330 | 4.705971 | 0.081211 | -0.778344 |